


'''simple docstring'''



import string
from math import logaa







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       List[str]   =  document.translate(
			    str.maketrans(""""""			,   """"""			,   string.punctuation       )       ).replace("""\n"""			,   """"""       )
			UpperCAmelCase__	:       Optional[int]   =  document_without_punctuation.split(""" """       )  # word tokenization
			return len([word for word in tokenize_document if word.lower() == term.lower()]       )







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Dict   =  corpus.lower().translate(
			    str.maketrans(""""""			,   """"""			,   string.punctuation       )       )  # strip all punctuation and replace it with ''
			UpperCAmelCase__	:       Dict   =  corpus_without_punctuation.split("""\n"""       )
			UpperCAmelCase__	:       List[Any]   =  term.lower()
			return (len([doc for doc in docs if term in doc]       ), len(lowerCamelCase_       ))







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__=False       ):
			if smoothing:
						if n == 0:
									raise ValueError("""log10(0) is undefined."""       )
						return round(1 + logaa(n / (1 + df)       )			,   3       )

			if df == 0:
						raise ZeroDivisionError("""df must be > 0"""       )
			elif n == 0:
						raise ValueError("""log10(0) is undefined."""       )
			return round(logaa(n / df       )			,   3       )







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			return round(tf * idf			,   3       )


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






